Control aerial movement of drone based on line-of-sight of humans using devices

ABSTRACT

Examples disclosed herein relate to control of a drone. In one example, aerial movement of the drone is controlled. In the example, it is determined, based on a plurality of devices, whether the drone is within a line-of-sight with at least a respective one of a plurality of humans within a physical proximity to a respective one of a the devices. In the example, the devices are used by the drone to track the humans. In the example, when the drone is determined to lack the line-of-sight, aerial movement of the drone is controlled to move the drone to become within the line-of-sight.

BACKGROUND

Drones are unmanned aerial vehicles. Some drones can be controlledautonomously by onboard computers while other drones can be controlledvia remote control or other means. Drones can be used for a wide arrayof functionality, from recreational use to commercial use to militaryuse.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description references the drawings, wherein:

FIG. 1 is a system including a drone capable of tracking humans based ondevices and control the drone to be within line-of-sight of at least oneof the devices, according to an example;

FIG. 2 is a system including drones capable of tracking humans based ondevices and control the drone to be within line-of-sight of at least oneof the devices, according to an example;

FIG. 3 is a flowchart of a method for controlling a drone to be within aline-of-sight of a human based on a wearable device on the human, wherethe wearable device is tracked, according to an example;

FIG. 4 is a block diagram of a drone capable of tracking humans based onwearable devices and controlling aerial movement of the drone to staywithin a line-of-sight of at least one of the humans, according to anexample; and

FIG. 5 is a flowchart of a method for controlling a drone to be within aline-of-sight of a human based on a wearable device, where an alert isprovided if a rule is triggered based on location information, accordingto an example.

DETAILED DESCRIPTION

Recent development of drones unlocks a number of opportunities to betterlife for mankind. Drones can be used for various functions, such ashelping optimize crop yield on a farm, monitoring children at a school,monitoring prison inmates, recreation, etc.

Tracking individual humans with drones can be useful for variousreasons. For example, parents may prefer to know the whereabouts oftheir children. Individuals may be concerned about their elderly parentsor loved ones and may wish to track them, with their consent. Further,people may be more comfortable using a car service if a drone trackingsystem was able to confirm their whereabouts.

Governments may decide to provide safety rules for usage of drones. Forexample, governments may choose to make a rule to require line-of-sightfrom a human being to the drone, may require that an operator be able tocontrol the drone, make a rule to limit speed of the drone, providerules for the altitude of the drone, limit particular airspace, etc.

Drones can include multiple sensors. Further, people can have devices(e.g., wearable devices) within a threshold proximity that can help thedrone track and monitor the people. Moreover, the devices can also beused to confirm line-of-sight between a human being and a drone. In someexamples, information from drones and the devices can be sent to acomputing system (e.g., a cloud computing system). The computing systemcan be used to provide tracking alerts or other services frominformation provided by the drones and/or wearable devices.

In some examples, an entity that controls the drone can use acontrolling device, such as a handheld controller with communication tothe drone, a mobile phone, another wearable device, etc. to communicatewith the drone to control the drone. Further, in some examples, thecommunication can be routed (e.g., via the Internet) to the drone.Moreover, in some examples, the computing system can be used to controlthe drone via a communication infrastructure (e.g., using cellularcommunications).

In some examples, sensory information can be collected from the devicesassociated with humans and the drones to allow the computing system toprovide services. For example, a cloud-based application can provide aninformation hub for subscribers. People can register their devices tothe service. When certain conditions are met (e.g., the device is on andat a particular location), the device can provide information to thecomputing system. The computing system can also coordinate with thedrones. Thus, the computing system can act as an information huballowing for processing of the data to provide services, such asnotifications, data retention (e.g., via video from the drone, locationof the drone, location information about the devices from the droneand/or the devices themselves, etc.). Moreover, alerts can be integratedinto existing systems via an Application Programming Interface (API).Example systems can include emergency management systems (EMS) such asAmber Alert or other emergency notification systems. As such, if thedrone is tracking a child at a school and the child is moved outside ofa boundary associated with the school, the drone can follow the childand an alert can go out (e.g., to the parent, the school, an entitycontrolling the drone, combinations thereof, etc.). Similar integrationscan occur for proprietary systems (e.g., an alert to a prison guard fora prison security context).

FIG. 1 is a system including a drone capable of tracking humans based ondevices and control the drone to be within line-of-sight of at least oneof the devices, according to an example. The system 100 can include adrone 110 that communicates with devices 150 a-150 n. Communications canoccur via a communication network (e.g., via network connections, viathe Internet, using cell technologies, etc.), transmissions from thedevices to and from the drone (e.g., using radio frequencytransmissions), etc. In certain examples, the drone 110 includescomponents to perform aerial movement, sensors, and computingcomponents. For example, a navigation engine 120 can be used to controlmovement of the drone 110. Further, the line-of-sight engine 122 can usesensor information to determine whether each of the devices 150 a-150 nassociated with respective humans 152 a-152 n and/or the humans 152a-152 n are within line-of-sight. The use of sensors can be used forvarious activity by the drone 110.

FIG. 2 is a system 200 including drones capable of tracking humans basedon devices and control the drone to be within line-of-sight of at leastone of the devices, according to an example. In various examples,line-of-sight represents the visibility of a human to the drone.Line-of-sight can be defined by criteria used by the drone. The system200 can include drone 110 as well as drones 210 a-210 m, the devices 150associated with respective humans 152 a-152 n, a drone control device160 to control one or more of the drones 110, 210, and a platform 170that can provide services based on information provided by the drones110, 210 and/or devices 150 a-150 n. In some examples, the drone 110 canalso include rules 124, a location engine 126, sensors 128, a trackingengine 130, an alert engine 132, and aerial components 134. Moreover,the drone 110 may include at least one processor 230, memory 232, andinput/output interfaces 234.

The navigation engine 120 can control aerial movement of the drone byusing aerial components. In some examples, aerial components 134 caninclude one or more motors to control moving parts of the drone. Forexample, the navigation engine 120 can control the motors to turnpropellers to control drone movement. Various technologies can be usedto implement drone motion (e.g., by creating aerodynamic forces tocreate lift of the drone). Examples of types of drones can includehelicopter drones, quadcopter drones, plane drones, etc. Drones can alsocome in various sizes. For example, a drone may be a large size (e.g.,more than 55 pounds), can be a smaller size (e.g., under 55 pounds) thatmay be regulated under different rules, and even smaller size (e.g.,less than 4.4 pounds), etc. Size examples can vary, and can bedetermined based on one or more regulations from a government entity(e.g., the Federal Aviation Administration).

The line-of-sight engine 122 can be used to determine whether the droneis within line-of-sight with at least one of the devices 150 a-150 nwithin a physical proximity to a human 152 a-152 n. As used herein,physical proximity to a human means that the device is located on thehuman (e.g., a cell phone in a pocket, a smart device in a hat, a smartwatch, a bracelet, smart glasses, etc.). In some examples, the devicesare wearable devices. Wearable devices are clothing and accessoriesincorporating computer or advanced electronic technologies. In someexamples, the devices 150 a-150 n are located external to the human. Forexample, a hat may include technology to allow the drone 110 todetermine that there is line-of-sight with the human and/or the device150.

In one example, the device 150 can include global positioning system(GPS) technology. The GPS technology can be used to determine a positionof the device. The position can be provided to the drone 110 eitherdirectly via input/output interfaces 234 or via a platform 170. Inanother example, the device 150 may include a locator (e.g., a beaconsuch as a radio beacon, an infrared beacon, Wi-Fi beacon, etc.). Drones110, 210 can use the beacon to determine the position of the device 150and/or human (e.g., via triangulation). In some examples, sensors frommultiple drones can be used to triangulate the position of a human.Moreover, a combination of technologies can be used to determine whetherthe drone 110 has a line-of-sight with the respective devices 150 and/orthe associated human 152. For example, the drone 110 may use the GPS orbeacon technology to determine the general location of the device and/orhuman and then use a sensor 128 (e.g., an image sensor, an infraredsensor, etc.) to confirm that there is a line-of-sight between the drone110 and the respective device and/or human. In some examples, aninfrared beacon can be used to confirm line-of-sight.

In one example, the location of the device (e.g., based on GPScoordinates) can be used to determine a place for the drone 110 to lookfor a human. In this example, the drone 110 can use a sensor 128, suchas an optical sensor, to look for a human in that area. Recognitiontechnology can be used to determine whether there is a human in thatarea. If so, then there is line-of-sight to that human.

In some examples, the device itself can be used for line-of-sightdetermination in one step. For example, the device can send out a beaconthat can be received by the drone 110 if there is line-of-sight (e.g.,an infrared beacon). If the drone 110 receives the signal, then there isline-of-sight. These beacons can be configured to provide a signal thatlasts a particular range. As such, distance can be taken into account.In some examples, the line-of-sight determination can be based online-of-sight based communications.

The tracking of the devices/humans can be used for dual purposes. Thefirst purpose is to track the respective humans (e.g., to ensure alocation of the humans 152 are within particular parameters). The secondpurpose is to ensure that drones 110 are within line-of-sight of atleast one human.

In some examples, if the line-of-sight engine 122 determines that thereis no line-of-sight, the drone 110 can be controlled via the navigationengine 120 to become within line-of-sight of at least one human. Rules124 can be used to specify, when the drone 110 is not withinline-of-sight. For example, the rules 124 can include a rule thatspecifies distance criteria such as a threshold distance between thedrone 110 and the devices. As noted, the location of the devices150/humans 152 can be determined. Also, the location of the drone 110can be determined. In some examples, the location of the drone 110 canbe maintained using sensors 128 (e.g., accelerometer, gyroscope,compass, GPS, cell tower tracking, other positioning systems, altimeter,thermal sensors, combinations thereof, etc.). The location engine 126can determine the location of the drone and a location of the respectivedevices 150. The distance criteria can be a customizable criteria thatindicates a distance that can be deemed to be associated with a lack ofline-of-sight. In one example, the criteria can take into account a sizeof the drone 110 and a sighting capability of a human being (e.g., at aparticular visual acuity). In another example, the criteria may alsotake into account dynamic visual controls, such as weather.

If the criteria is satisfied, the particular human and associated devicecan be considered to possibly lack a line-of-sight with the drone 110.Thus, a line-of-sight determination can be based on the criteria.Further, in some examples, the criteria can be used to determine apotential lack of line-of-sight and another sensor 128 can be used toconfirm a lack of line-of-sight. For example, the distance criteria canbe used to determine whether there is a potential lack of line-of-sightand an image sensor, infrared sensor, etc. can be used to confirm a lackof line-of-sight or confirm that there is a line-of-sight.

In other examples, three-dimensional maps of the terrain can be used todetermine whether the location a human is at has line-of-sight with thedrone 110 based on obstructions. This can be based on a land location ofthe user and any obstructions. In some examples, sensor data (e.g.,image data, sonar data, etc.) taken by the drones 110, 210 can be usedto determine the three-dimensional maps. In some examples, thethree-dimensional map processing can be accomplished at the drones 110.In other examples, the three-dimensional map processing can beoff-loaded to a platform 170, such as a cloud system or computingsystem.

In some examples, rules 124 can include an action to take by the drone110 based on what criteria has been fulfilled. In one example, the drone110 can be caused to return to a line-of-sight of at least one of thedevices 150 based on a determination of a potential lack ofline-of-sight or a confirmed lack of line-of-sight. The rules 124 mayfurther specify where the drone 110 is to go. For example, the drone 110can be instructed to move to within a certain distance of one of thedevices 150 (e.g., a closest one of the devices 150). In other examples,the drone 110 can have a pre-determined path to take, a dynamic path totake, a static area to patrol, a dynamic area to patrol (e.g., based onlocations of the devices 150), etc. In one example, the drone 110 may beinstructed to move to within a certain location or distance from one ofthe devices 150 within its future path. In some examples, the lack ofline-of-sight is based on a determination that one of the devices iswithin the criteria and the other devices are already determined to lackthe line-of-sight.

In one example, one of the rules 124 can specify boundary criteria forthe humans 152 and/or devices 150. The tracking engine 130 can determinewhether the location of a device meets the boundary criteria. In someexamples, the boundary criteria can include a set of locationcoordinates that can be mapped. The alert engine 132 can determine analert based on criteria, such as the boundary criteria. If a respectivedevice 150 is outside of the boundary or within a boundary threshold, analert can be set. The alert can cause the drone 110 to go to therespective device 150.

An example of use of boundary criteria may be to monitor children at aschool or playground. If the child moves past the boundary, particularactions can be taken by the drone 110, such as the navigation engine 120moving the drone 110 towards the child, sending an alert to a user(e.g., registered parent), sending causing video to start recording andtarget the child, etc. In a similar case, the devices 150 and drones110, 210 can be used to track inmates.

In another example, the drones 110, 210 can be used for agriculture. Thedrones 110, 210 can be used to track worker movement while alsoperforming other activities. For example, the drones can be used tospray pesticides, irrigate, etc. over a portion of a field. The devices150 can be used to ensure that humans 152 are not in the field duringspray. Further, the line-of-sight engine 122 can be used to ensure thatproper supervision of the drone occurs. In some examples, one or more ofthe drones 110, 210 can be controlled by a drone control device 160(e.g., a remote control, a mobile device using an app, etc.).

The rules 124 can be used to implement other functionality. For example,a rule 124 can specify conditions that show that a respective device 150is not within proximity of an associated human 152. For example, thedevice 150 may include accelerometer information that can be sent to thedrone 110 and/or platform 170. The accelerometer information can becompared to a profile or other function to determine whether anomalousbehavior is present. One example of anomalous behavior includes nomotion from the device 150. A device 150 located on a human 152 wouldshow some motion (e.g., from breathing). Therefore, the lack of anymovement could show that the human 152 is no longer associated with thedevice 150. The rule 124 can further specify that in response to thecondition occurring, the navigation engine 120 controls aerial movementof the drone 110 towards a location of the device 150 and/or human 152based on the recognition that the device is not within proximity of thehuman 152.

Other rules can be implemented, for example, to ensure that the drone110, 210 meets government regulations. In one example, the drone 110,210 can include an altimeter and the drone 110, 210 can have an altituderange to fly within. In another example, the drone 110, 210 can includerules 124 to keep the drones 110, 210 from flying directly overhead of ahuman. Rules 124 can be used in conjunction with sensors 128 to providevarious navigation adjustments. In one example, the platform 170 canimport government regulations and base the rules based on the governmentregulations.

In some examples, the platform 170 can be a computing system, such as acloud computing system that can be used to communicate (e.g., with thedrone 110, with devices 150, with other devices, etc.) via acommunication engine 174. Various functionality described herein asbeing performed by the drone 110 can be offloaded to the platform 170.For example, the location of the devices 150 a-150 n can be determinedby the platform 170. Further, information about the drones 110, 210 canbe sent to the platform 170 via a communication engine 174. Theinformation can include locations, navigation programming, etc.Moreover, the information from the devices 150 can be sent to thecommunication engine 174. The information can include location of thedevices, other sensor information (e.g., accelerometer information),etc. The platform 170 can perform data analytics on the information todetermine whether one or more rules or alerts are triggered. If certainrules are triggered, the control engine 176 can send the drone 110instructions to move accordingly (e.g., to a device location, within aboundary, etc).

In other examples, if a rule is triggered to alert a user, an alert canbe sent to the user. In one example, the subscription engine 172 can beused to register users to a database. The database can include devices150 and/or humans 152 that the registered user is interested in. If analert occurs, the user can be sent an alert. In the example of a schoolsetting, the user can be an administrator at the school, a parent, etc.In the prison example, the user can be a warden, a prison guard, etc.Similar examples can be used for tracking others such as elderly people,disabled people, etc. Users can register devices 150 with humans 152 andan alert location (e.g., an email address, a phone number, etc.). Whencriteria associated with an alert is met, the subscription engine 172can cause sending of an alert to the alert location. Further, in somescenarios, other information can be provided such as a video feed of thehuman 152, control over a drone looking for the human, etc.

As noted above, the alerts can be sent to an emergency alert system. Assuch, the communication engine 174 can use APIs to communicate thealerts to systems associated with the triggered rule. For example, in arule context of a missing child, an API associated with an Amber Alertsystem can be used.

With the approaches used herein, multiple drones 110, 210 a-210 m can beused to monitor multiple humans 152 a-152 n. The drones 110, 210 can becoordinated via navigation engines and/or a platform 170 that cancentralize control. Multiple drones 110, 210 can be used to track humans152 associated with devices 150 (e.g., wearable devices) as well as tokeep within line-of-sight of at least one of the humans 152. This canensure that the drone is supervised while also ensuring that the humansare tracked.

A communication network can be used to connect one or more of the drones110, 210, devices 150, platform 170, other devices, etc. Thecommunication network can use wired communications, wirelesscommunications, or combinations thereof. Further, the communicationnetwork can include multiple sub communication networks such as datanetworks, wireless networks, telephony networks, etc. Such networks caninclude, for example, a public data network such as the Internet, localarea networks (LANs), wide area networks (WANs), metropolitan areanetworks (MANS), cable networks, fiber optic networks, combinationsthereof, or the like. In certain examples, wireless networks may includecellular networks, satellite communications, wireless LANs, etc.Further, the communication network can be in the form of a directnetwork link between devices. Various communications structures andinfrastructure can be utilized to implement the communicationnetwork(s). Moreover, devices 150 and drones 110, 210 can have multiplemeans of communication.

By way of example, devices can communicate with each other and othercomponents with access to the communication network via a communicationprotocol or multiple protocols. A protocol can be a set of rules thatdefines how nodes of the communication network interact with othernodes. Further, communications between network nodes can be implementedby exchanging discrete packets of data or sending messages. Packets caninclude header information associated with a protocol (e.g., informationon the location of the network node(s) to contact) as well as payloadinformation.

The engines 120, 122, 126, 130, 132, 172, 174, 176 include hardwareand/or combinations of hardware and programming to perform functionsprovided herein. Moreover, the modules (not shown) can includeprogramming functions and/or combinations of programming functions to beexecuted by hardware as provided herein. When discussing the engines andmodules, it is noted that functionality attributed to an engine can alsobe attributed to the corresponding module and vice versa. Moreover,functionality attributed to a particular module and/or engine may alsobe implemented using another module and/or engine.

A processor 230, such as a central processing unit (CPU) or amicroprocessor suitable for retrieval and execution of instructionsand/or electronic circuits can be configured to perform thefunctionality of any of the engines described herein. In certainscenarios, instructions and/or other information, such as locationinformation, registration information, etc., can be included in memory232 or other memory. Input/output interfaces 234 may additionally beprovided by the drone 110. Moreover, in certain embodiments, somecomponents can be utilized to implement functionality of othercomponents described herein. Input/output devices such as communicationdevices like network communication devices or wireless devices can alsobe considered devices capable of using the input/output interfaces 234.

Each of the modules may include, for example, hardware devices includingelectronic circuitry for implementing the functionality describedherein. In addition or as an alternative, each module may be implementedas a series of instructions encoded on a machine-readable storage mediumof a computing device and executable by a processor. It should be notedthat, in some embodiments, some modules are implemented as hardwaredevices, while other modules are implemented as executable instructions.

FIG. 3 is a flowchart of a method for controlling a drone to be within aline-of-sight of a human based on a wearable device on the human, wherethe wearable device is tracked, according to an example. FIG. 4 is ablock diagram of a drone capable of tracking humans based on wearabledevices and controlling aerial movement of the drone to stay within aline-of-sight of at least one of the humans, according to an example.

Although execution of method 300 is described below with reference todrone 400, other suitable components for execution of method 300 can beutilized (e.g., drones 110, 210). Additionally, the components forexecuting the method 300 may be spread among multiple devices (e.g.,part of the functionality may be accomplished on the drone and part ofthe functionality may be offloaded to a cloud system). Method 300 may beimplemented in the form of executable instructions stored on amachine-readable storage medium, such as storage medium 420, and/or inthe form of electronic circuitry.

The drone 400 includes, for example, a processor 410, and amachine-readable storage medium 420 including instructions 422, 424, 426for controlling the drone 400 according to rules and information aboutwearable devices located on humans.

Processor 410 may be at least one central processing unit (CPU), atleast one semiconductor-based microprocessor, at least one graphicsprocessing unit (GPU), other hardware devices suitable for retrieval andexecution of instructions stored in machine-readable storage medium 420,or combinations thereof. For example, the processor 410 may includemultiple cores on a chip, include multiple cores across multiple chips,multiple cores across multiple devices (e.g., between the drone and acloud system), or combinations thereof. Processor 410 may fetch, decode,and execute instructions 422, 424, 426 to implement tracking of userswith wearable devices and changing aerial movement based on aline-of-sight with one or more of the users/wearable devices. As analternative or in addition to retrieving and executing instructions,processor 410 may include at least one integrated circuit (IC), othercontrol logic, other electronic circuits, or combinations thereof thatinclude a number of electronic components for performing thefunctionality of instructions 422, 424, 426.

Machine-readable storage medium 420 may be any electronic, magnetic,optical, or other physical storage device that contains or storesexecutable instructions. Thus, machine-readable storage medium may be,for example, Random Access Memory (RAM), an Electrically ErasableProgrammable Read-Only Memory (EEPROM), a storage drive, a Compact DiscRead Only Memory (CD-ROM), and the like. As such, the machine-readablestorage medium can be non-transitory. As described in detail herein,machine-readable storage medium 420 may be encoded with a series ofexecutable instructions for controlling aerial movement of a drone basedon a location of a wearable device.

At 302, aerial movement instructions 424 can be executed by processor410 to control the drone 400 (e.g., by controlling aerial components ofthe drone). The drone 400 can be controlled using programmedinstructions executed by the processor 410. For example, the drone 400can be set to patrol an area, can be set to follow a pattern, can be setto dynamically alter the patrol or pattern based on conditions (e.g.,movement of tracked devices, weather, etc.), or the like. Further, insome examples, the drone 400 can receive other control instructions froma control unit (e.g., a remote control, a remote application on a smartdevice, etc.).

The drone 400 can be used to track humans using wearable devices. Forexample, line-of-sight instructions 422 can be executed by processor 410to determine whether the drone 400 is within a line-of-sight of thewearable device and/or the respective humans (304). The drone 400 canuse this information to determine whether the drone 400 is withinline-of-sight of at least one of the humans. Further, the drone 400 cantrack the respective humans using the wearable devices.

At 306, the drone 400 can determine that it is within a buffer distancefrom one of the wearable devices indicative of a possible lack ofline-of-sight to the wearable device and that there is a lack ofline-of-sight from the other wearable devices. As used herein, the term“possible lack of line-of-sight” means that the drone does not lack theline-of-sight, but is within the buffer distance and/or the drone doeslack the line-of-sight. The determination can be according to a rule andsensor information. In one example, the lack of the line-of-sight of theother wearable devices can be based on location information receivedfrom the wearable devices, sensor data captured at the drone,combinations thereof, etc. In one example, a certain distance can beindicative of a lack of line-of-sight. In another example, the distancecan be augmented by weather conditions (e.g., fog, cloudiness, etc.). Ina further example, the lack of line-of-sight can be determined based ona visual or infrared sensor on the drone 400, laser communicationbetween the drone 400 and wearable devices, etc. In one example, thewearable device is a head device, such as a cap or helmet. The headdevice can include a beacon that can be used to facilitate theline-of-sight determination.

The buffer distance is a threshold distance that is smaller in valuethan a distance indicative of a lack of line-of-sight. The bufferdistance can be used to cause augmentation of the drone's path beforethe drone 400 has a lack of line-of-sight from the one wearable device.As such, at 308, the aerial movement instructions 424 can be executed tomove the drone 400 towards the wearable device. This ensures that atleast one of the many wearable devices is within a line-of-sight of thedrone. In some examples, government regulations may specify that aline-of-sight between a human and a drone be maintained.

In one example, one of the wearable devices can be selected based on atriggered rule by executing selection instructions 426. As noted above,such triggers can include the wearer of the wearable device movingoutside of a boundary, indications that the wearable device is no longerassociated with the wearer, a stoppage of communication from thewearable device, etc. The processor 410 can determine a location of thewearable device (e.g., based on GPS coordinates, other locationinformation, etc.). Aerial movement instructions 424 can be executed tocause the drone 400 to move towards the location. In some examples, thelocation can be updated and the drone can follow the wearable device. Inone example, the wearable device is followed until a manual controlsignal is received from a control device (e.g., a remote control).

As noted above, alerts can be associated with rules. As such, in oneexample, when a rule is triggered, an alert can be sent. As noted above,the alert can be sent to registered user devices.

FIG. 5 is a flowchart of a method for controlling a drone to be within aline-of-sight of a human based on a wearable device, where an alert isprovided if a rule is triggered based on location information, accordingto an example. Although execution of method 500 is described below withreference to a computing system, other suitable components for executionof method 500 can be utilized (e.g., platform 170, other computingdevices, cloud computing systems, etc.). Additionally, the componentsfor executing the method 500 may be spread among multiple devices.Method 500 may be implemented in the form of executable instructionsstored on a machine-readable storage medium, such as storage medium,and/or in the form of electronic circuitry.

As noted above, a drone can be configured to move throughout a static ordynamic area. The computing system can control the drone's motions(e.g., by sending programming for the drone to execute). At 502, thecomputing system receives location information about multiple wearabledevices that are monitored by a drone via aerial monitoring. Thewearable devices can be worn by the respective humans and thus be withina physical proximity to the human. The wearable devices can beregistered to alerts. The alerts may include an identifier of thewearable device and/or a human associated with the wearable device, arule associated with when the alert should occur, and contactinformation (e.g., an email address, a phone number, Internet address,etc.) to send the alert. As noted above, the alerts can be sent inresponse to registration for the alerts.

For example, a parent may be interested in tracking their child atschool and can have an alert associated with when the child is within aparticular distance to/from a school boundary, when the child is acertain distance from a teacher wearing another wearable device, etc.The drone can be controlled to stay within a line-of-sight of at leastone of the wearable devices from multiple wearable devices (e.g.,wearable devices respectively associated with particular children).

At 504, the computing system determines that one of the wearable devicesis acting according to a triggered rule based on the received locationinformation. As noted above, various rules can be used. Further, alertscan be associated with the rules. As such, at 506, when a rule istriggered, the computing system can provide an associated alert for therule (e.g., based on registration for the alert). For example, the alertcan be based on a determination that the wearable device is outside of aboundary associated with the triggered rule based on the locationinformation of the wearable device (e.g., notify a registered user thata child is outside of a school boundary).

In another example, the triggered rule can alert a user that the droneis outside of a line-of-sight of at least one of the wearable devices oris in a buffer distance from at least one of the wearable devices thatindicates a possible lack of line-of-sight from the set of wearabledevices. In one example, the rule can indicate that if each of thewearable devices is out of the line-of-sight of the drone and/or withina threshold distance away (e.g., at a buffer range), the rule istriggered. The drone can then be controlled to move towards one of thewearable devices. The wearable device to move towards can be selectedbased on criteria (e.g., the closest wearable device to the drone, aclose wearable device within a path the drone is on, etc.). Thecomputing system can send a control command to cause the drone to movetowards the selected wearable device.

What is claimed is:
 1. A drone comprising: a navigation engine tocontrol aerial movement of the drone; a line-of-sight engine todetermine, based on a plurality of devices, whether the drone is withina line-of-sight with at least a respective one of a plurality of humanswithin a physical proximity to a respective one of the plurality ofdevices, wherein the devices are used by the drone to track the humans,and wherein when the drone is determined to lack the line-of-sight, thenavigation engine is further to control the aerial movement of the droneto become within the line-of-sight.
 2. The drone of claim 1, furthercomprising: a rule specifying when the drone is not within theline-of-sight, wherein the determination is based on the rule.
 3. Thedrone of claim 2, further comprising: a location engine to determine afirst location of the drone and a second location of the at least one ofthe devices, wherein the rule indicates a distance criteria between thefirst location and the second location; and wherein the line-of-sightdetermination is further based on the distance criteria, the firstlocation, and the second location.
 4. The drone of claim 3, furthercomprising: a sensor to target the second location to confirm the lackof the line-of-sight for the lack of line-of-sight determination.
 5. Thedrone of claim 3, wherein the second location is determined based on atleast one of: a radio frequency signal, global positioning systeminformation, and an optical sensor.
 6. The drone of claim 3, wherein thedevices are wearable devices located on the respective human.
 7. Thedrone of claim 6, further comprising: a second rule specifying boundarycriteria for the respective one of the plurality of devices; and atracking engine to track whether the second location meets the boundarycriteria, wherein the navigation engine is further to control the aerialmovement of the drone to move towards the respective one of theplurality of devices based on the tracking.
 8. The drone of claim 1,further comprising: an alert engine to recognize the respective one ofthe plurality of devices is not within the proximity of the respectivehuman, wherein the navigation engine controls the aerial movement of thedrone towards a location of the respective one of the plurality ofdevices based on recognition that the respective one of the plurality ofdevices is not within the proximity of the respective human.
 9. Anon-transitory machine-readable storage medium storing instructionsthat, if executed by at least one processor of a drone, cause the droneto: control aerial movement of the drone throughout an area; determinewhether the drone is within a line-of-sight with at least one of aplurality of humans each respectively within a physical proximity to arespective one of a plurality of wearable devices; determine that thedrone is within a buffer distance, from a first one of the plurality ofwearable devices, indicative of a possible lack of the line-of-sightfrom the first one of the plurality of wearable devices and lacks theline-of-sight with others of the plurality of wearable devices; andcontrol aerial movement of the drone towards the first one of theplurality of wearable devices.
 10. The non-transitory machine-readablestorage medium of claim 9, further comprising instructions that, ifexecuted by the at least one processor, cause the drone to: select asecond one of the plurality of wearable devices based on a triggeredrule; determine a location of the second one of the plurality ofwearable devices; and control aerial movement of the drone towards thesecond one of the plurality of wearable devices.
 11. The non-transitorymachine-readable storage medium of claim 10, further comprisinginstructions that, if executed by the at least one processor, cause thedrone to: send an alert based on the triggered rule.
 12. Thenon-transitory machine-readable storage medium of claim 11, furthercomprising instructions that, if executed by the at least one processor,cause the drone to; follow the second one of the plurality of wearabledevices until a manual control signal is received from a control device.13. A method comprising: receiving, at a computing system, locationinformation about a plurality of wearable devices to be monitored by adrone via aerial monitoring, wherein the wearable devices arerespectively registered to particular alerts, wherein the wearabledevices are each respectively within a physical proximity to a human,wherein the drone is controlled to stay within a line-of-sight of atleast one of the humans based on the wearable devices; determining thata first of the wearable devices is acting according to a triggered rulebased on the location information; and providing, by the computingsystem, one of the particular alerts associated with the first of thewearable devices.
 14. The method of claim 13, further comprising:controlling aerial movement of the drone throughout an area; determiningthat the drone is within a buffer distance from the at least one of thewearable devices indicative of a possible lack of the line-of-sight fromthe wearable devices; and causing the drone to move towards one of thewearable devices.
 15. The method of claim 13, wherein the one of theparticular alerts is based on a determination that a location of thefirst of the wearable devices is outside of a boundary associated withthe triggered rule based on the location information.